Tomasz Pander

New polynomial approach to myriad filter computation

The myriad filter is a robust filter which is very useful in suppressing impulsive noise. It belongs to the family of robust M-filters and is controlled with one parameter only. The myriad filter is defined as a running window filter whose output is the sample myriad of the elements in the window. The most popular approach to determination of myriad filter output is based on the fixed point method. However, in this case time of computation is relatively long, which limits on-line applications. In this paper, a faster method of the myriad filter computation is presented. It performs the second order polynomial fitting and the next x-coordinate of the parabola top is searched. The proposed method can operate on different types of impulsive noise, requires less computational time and is equally robust as the fixed point method or the branch-and-bound search method. The presented method is applied to process a chirp signal in impulsive environment. The obtained results are compared to the fixed-point algorithm and the branch-and-bound searching method.

An application of detection function for the eye blinking detection

The electrooculogram represents the electrical activity of muscles which steering of movements of an eye. The eye blinking is a natural protection system which defends the eye from environmental exposure. The spontaneous eye blink is considered to be a suitable indicator for fatigue diagnostics during many, different tasks of human being activity. The detection function is used to detect the spontaneous eye blink action. On this base the position of an eye blink is estimated. The results demonstrate that the measurement of an eye blink parameter provides reliable information for eye-controlled systems from human-machine interface.

Averaging of nonlinearly aligned signal cycles for noise suppression

Abstract Averaging of nonlinearly aligned (time-warped) signal cycles is an important method for suppressing noise of quasi-periodical or event related signals. However, in the paper we show that the operation of time warping introduces unfavorable violation of the requirements that should be satisfied for effective averaging and, as a result, it causes poor suppression of noise. To limit these effects, we redefine the matrix of the alignment costs. To improve results of averaging in cases of variable energy noise, we apply weighting of the summed signal samples. The derived formula gives smaller weights for more noisy signal cycles and this way limits their influence on the constructed template. The proposed modifications caused a significant increase of the Noise Reduction Factor (NRF) in the experiments on the simulated evoked potentials. Whereas the greatest NRF obtained by the reference methods in nonstationary white noise environment was equal to 1.55, for the new method proposed we achieved a value of 4.44. For non-stationary colored noise, the corresponding values were 1.44 and 2.99. Moreover, application of the developed method to ECG signal processing, prior to the measurements of the QT interval, significantly improved the measurements immunity to noise.

A new method of saccadic eye movement detection for optokinetic nystagmus analysis

The analysis of eye movements is valuable in both clinical work and research. One of the characteristic type of eye movements is saccade. The accurate detection of saccadic eye movements is the base for further processing of saccade parameters such as velocity, amplitude and duration. This paper concerns an accurate saccade detection method that is based on pre-processing signal and then the proposed non-linear detection function can be applied. The described method characterizes less sensitivity for any kind of noise due to an application of the robust myriad filter which is used to eliminate baseline drifts and impulsive artifacts. The congenital nystagmus is one of the field where our method can be applied to detect saccades. The proposed detection function is computationally efficient and precisely determines the time position of saccadic eye movements even when the signal-to-noise ratio is low. The presented method may have potential application in automatic ENG signal processing systems for determining visual acuity.

An Application of Myriad M-Estimator for Robust Weighted Averaging

The method of signal averaging is such technique that allows the repeated or periodic waveforms which are contaminated by noise to be enhanced. The most often used operation for averaging is the arithmetic averaging and its different variations. Unfortunately the mean operator is sensitive for outliers. In this work the well known myriad M-estimator is applied for averaging. The myriad weighted averaging allows to suppress the impulsive type of noise. In order to evaluate the proposed method, artificial impulsive noise is generated with using the symmetric α-stable distributions. The impulsive noise component is added to the deterministic signal with known value of geometric signal-to-noise ratio (GSNR) which is equivalent of ordinary SNR. The experiments show usefulness of the proposed method for weighted averaging of periodic signals like ECG signal.

Fuzzy Approach to Saccades Detection in Optokinetic Nystagmus

The electronystagmography (ENG) based analysis of the nystagmus provides valuable information about condition of the human vision system. The typical ENG signal corresponding to nystagmus has a form of a saw tooth waveform with slow and fast (saccade) components. The slow component is related to the stimulus while the saccade refers to a rapid reset of eye position. The accurate detection of saccadic eye movements is inevitable when determining the nystagmus characteristic. The paper presents saccades detection method that is based on fuzzy clustering. The proposed procedure is computationally efficient and allows for precise determination of the saccade position in the time domain.

An automatic saccadic eye movement detection in an optokinetic nystagmus signal.

Abstract A saccade is one of the characteristic types of eye movements. The accurate detection and location of saccades in the signal representing the movement activity of the eyes are essential in medical applications. The main purpose of this paper is to present a new, robust approach to the detection of saccadic eye movements. The procedure is based on a so-called detection function, which is the result of the electronystagmographic (ENG) myriad signal filtering, nonlinear operation, and fuzzy median clustering. Smooth peaks of the detection function waveform correspond to the location of saccades in the ENG signal. The median fuzzy clustering-based method allows for calculating the amplitude threshold of the detection function, which improves the accurate saccade recognition. Both of these robust methods provide a two-step protection against outliers. The proposed algorithm was tested using artificial as well as real optokinetic nystagmus signals under different noise conditions. The results show the usefulness of the procedure when the precise detection and location of saccades are necessary.

An approach to the EOG signal segmentation based on fuzzy reasoning

In this paper we presented an approach to segmentation of an electrooculography (EOG) signal. For segmentation we have used the elements of the fuzzy set theory. Results obtained in our numerical experiments show usefulness of proposed approach. Our method can be also used for the generating of a learning set necessary for the neural networks or the fuzzy-neural systems training.

A Recovery of FHR Signal in the Embedded Space

In the paper we present a proposition of a recovery method for fetal heart rate signals (FHR). Recorded FHR signal often contains interruptions. The interruptions of the signal are caused among others by fetal movements. In such cases, the instantaneous heart rate determination is difficult because parts of the signal are missing. For the reconstruction of missing parts of the FHR signal, an approximation in an embedded space is proposed. The embedded space is created by applying the Taken’s theory. Next, the vicinity is determined for the missing samples (missing features in the embedded space). For the vicinity computation, two strategies are chosen: the nearest surrounding of the approximated sample or K nearest neighborhoods. The missing value is computed as a mean or a median. Numerical experiments performed for test (simulated) signals and the real FHR signal show advantages of proposed approach.

Myriad Filter Computation with 2nd Order Approximation Polynomial

The myriad filter is a robust filter which is very useful in suppressing impulsive noise. It belongs to the family of the robust m-filters and is controlled with one parameter only. The myriad filter is defined as a running window filter whose output is the sample myriad of elements in the window. In this paper, a fast and simple method of the myriad filter computation is presented. It performs the 2nd order polynomial fitting and the next x-coordinate of the parabola top is searched. The proposed method can operate on different types of impulsive noise, requires less computational time and is equally robust as the fixed point method or the branch-and-bound search method. The presented method is applied to process a chirp signal in the impulsive environment. The obtained results are compared to the fixed-point algorithm and the branch-and-bound searching method.